This research presents a bibliometric review of scientific production on the social and economic factors that influence mortality from tuberculosis between the years 2000 and 2024. The analysis covered 1742 documents from 848 sources, revealing an annual growth of 6% in scientific production with a notable increase starting in 2010, reaching a peak in 2021. This increase reflects growing concern about socioeconomic inequalities affecting tuberculosis mortality, exacerbated in part by the COVID-19 pandemic. The main authors identified in the study include Naghavi, Basu and Hay, whose works have had a significant impact on the field. The most prominent journals in the dissemination of this research are Plos One, International Journal of Tuberculosis and Lung Disease and The Lancet. The countries with the greatest scientific production include the United States, the United Kingdom, India and South Africa, highlighting a strong international contribution and a global approach to the problem. The semantic development of the research shows a concentration on terms such as “mortality rate”, “risk factors” and “public health”, with a thematic map highlighting driving themes such as “socioeconomic factors” and “developing countries”. The theoretical evolution reflects a growing interest in economic and social aspects to gender contexts and associated diseases. This study provides a comprehensive view of current scientific knowledge, identifying key trends and emerging areas for future research.

This study explores the transformative role of art design interventions in the sustainable development and infrastructure enhancement of intangible cultural heritage, with a particular focus on honored brands. The research develops a framework that positions aesthetic and interactive art design interventions as pivotal components in revitalizing these brands. Aesthetic interventions translate the brand’s core philosophy, spirit, and values into compelling visual symbols, harmonizing cultural heritage with modern image design to elevate brand reputation and consumer preference. Interactive interventions enhance user experience, particularly among younger demographics, by integrating technological and entertainment-based engagement, thereby strengthening consumer loyalty and brand influence. The study further investigates how these art design interventions serve as catalysts for broader social development, contributing to the modern relevance and societal impact of time-honored brands. Additionally, it examines the impact of these interventions on sustainable development, societal support, and policy alignment. By weaving together these elements, the research underscores the critical importance of aligning brand strategies with societal goals, fostering environments where brands actively contribute to social welfare and sustainable growth. The findings offer valuable theoretical insights and practical strategies for the sustainable development of time-honored brands, providing clear directions for future research and practice.

The study aims to explain the relationship between the effectiveness of a business and its management through the analysis of working capital. The findings prove the complementary relationship. The analysis of working capital will always have a significant impact on the effectiveness of business management. The main objective of any corporation is to be effective in business, which can be achieved by analyzing the working capital. The result shows that analysis of working capital based on factors like operational efficiency, the company’s earnings and profitability, cash management, corporate receivable management, and corporate inventory management creates room for improvement and effectiveness in business management. Firms might enhance finances for business expansion by lowering their working capital requirements. It has also been revealed that there is a considerable difference in industries across time. It was observed that there is a high association between working capital efficiency and firm profitability. A highly efficient corporation is less vulnerable to liquidity risk and is also self-sufficient in terms of external finance. Numerous studies have been done to regulate the true rapport between working capital investments and their impact on financial presentation. It demonstrates that effective investment in working capital management may boost profitability and business value. The relationship between accounting and finance was explained by measuring working capital management in demand to illustrate the status of profitability. It was suggested that accountants take a more professional approach to updating their accounting and finance skills in their organization through effective working capital management.

New technologies always have an impact on traditional theories. Finance theories are no exception to that. In this paper, we have concentrated on the traditional investment theories in finance. The study examined five investment theories, their assumptions, and their limitation from different works of literature. The study considered Artificial Intelligence (AI) and Machine Learning (ML) as representative of financial technology (fintech) and tried to find out from the literature how these new technologies help to reduce the limitations of traditional theories. We have found that fintech does not have an equal impact on every conventional finance theory. Fintech outperforms all five traditional theories but on a different scale.

Brunei Darussalam is a small Sultanate country with diverse forest cover. One of them would be Mangrove Forest. As it has four main administrative districts, Temburong would be the chosen case study area. The methods of collecting data for this article are by collecting secondary data from official websites and the map in this article (Figure 1) are showing the forest cover in Brunei Darussalam as of 2020. The aim of this article is to explain the mangrove forest especially at the Temburong District. As for the objectives, it would to be able to show the different types of forests in Temburong, hoping in ability to explain the different subtypes of mangroves forest and to explain in general the green jewel of Brunei Darussalam. Temburong has become the second highest tree coverage in Brunei Darussalam of 124 kha as of 2010, while the mangrove forest covering about 66% of total mangrove forest of 12,164 km² out of 18,418 hectares. Mangrove forest has seven subtypes: Bakau species, Nyireh bunga, Linggadai, Nipah, Nipah-Dungun, Pedada and Nibong. Selirong Forest Reserve and Labu Forest Reserve are the two-mangrove forest reserves in Brunei Darussalam at Temburong District. Forest cover in Brunei Darussalam are 3800 hectares as of 2020 and has lost its tree coverage of 1.17 kha and one of the reasons would be forest fire and the tree cover loss due to fire is around 197 ha and the district that has lost its tree cover mostly was at Belait District of total 13.4 kha between the year 2001 until 2022.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.